Motion correction with subspace-based self-navigation for combined angiography, perfusion and structural imaging
Bibliographic record
Abstract
Abstract Motion artifacts are problematic in many MRI modalities. “Self-navigating” approaches are desirable, since no additional scan time or hardware is required. However, the generation of a navigator image, to estimate and correct motion, is difficult in cases where the tissue contrast is changing during the navigator acquisition window, such as in magnetization- prepared methods. Here we propose a subspace approach to reconstruct accurate navigators in the presence of time-varying tissue contrast and apply it to a combined angiography, perfusion and structural imaging method using a golden ratio 3D cones trajectory. This arterial spin labeling-based pulse sequence relies on subtraction of label and control images to isolate the relatively weak blood signal, making it particularly susceptible to motion corruption. An inversion pulse leads to time-varying tissue contrast across the readout train, but by reconstructing subspace coefficient maps directly, artifacts due to the varying contrast were alleviated. This resulted in high-quality navigator images that were subsequently registered to estimate and correct for motion. In addition, a split-update method was proposed to efficiently reconstruct from mismatched label/control k-space data with locally low rank regularization enforced on the difference image. The correction process was tested with numerical simulation and in vivo data from 8 healthy subjects with and without cued motion. In numerical simulation, the subspace- based navigator achieved an 84% reduction in RMSE of residual motion compared to without motion correction. In vivo, motion correction resulted in noise-like and background artifacts being greatly reduced and vessel sharpness being noticeably improved. Correlation of angiography, perfusion and structural images with motion-free reference images also increased by 159%, 53% and 12%, respectively, after motion correction. These results show that subspace-based navigators can effectively improve the motion robustness of MR imaging in contrast-varying acquisitions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".